Dataconomy

MARCH 19, 2025

Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics. Data engineering lays the groundwork by managing data infrastructure, while data preparation focuses on cleaning and processing data for analysis.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91

Data Science Blog

FEBRUARY 4, 2023

If the data sources are additionally expanded to include the machines of production and logistics, much more in-depth analyses for error detection and prevention as well as for optimizing the factory in its dynamic environment become possible.

Cloud Data 147

Cloud Data Data Science Business Intelligence Business Intelligence 147

IBM Journey to AI blog

AUGUST 12, 2024

Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks. Their primary objective is to optimize and streamline IT operations workflows by using AI to analyze and interpret vast quantities of data from various IT systems.

Big Data 106

Big Data Big Data ML ML 106

ODSC - Open Data Science

FEBRUARY 2, 2023

Scale is worth knowing if you’re looking to branch into data engineering and working with big data more as it’s helpful for scaling applications. Scikit-learn also earns a top spot thanks to its success with predictive analytics and general machine learning.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

IBM Journey to AI blog

SEPTEMBER 19, 2023

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

phData

MAY 28, 2024

Cortex ML functions are aimed at Predictive AI use cases, such as anomaly detection, forecasting , customer segmentation , and predictive analytics. The combination of these capabilities allows organizations to quickly implement advanced analytics without the need for extensive data science expertise.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineering 52

AWS Machine Learning Blog

DECEMBER 15, 2023

Amazon SageMaker Canvas is a no-code ML workspace offering ready-to-use models, including foundation models, and the ability to prepare data and build and deploy custom models. In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics.

Machine Learning 120

Machine Learning Machine Learning AWS ML 120